Testing the Mechanisms, Layers, and Frequencies of Prediction Encoding and its Violation

测试预测编码的机制、层和频率及其违规

• 批准号: 10449136
• 负责人: Andre Moraes Bastos
• 金额: $ 24.7万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10449136
• 关键词: Architecture; Area; Behavior; Behavioral; Beta Rhythm; Biophysical Process; Biophysics; Brain; Brain Diseases; Cells; Code; Cognition Disorders; Communication; Computer Models; Coupled; Cues; Data; Disease; Electrophysiology (science); Environment; Failure; Feedback; Frequencies; Future; Goals; Grant; Impairment; Implanted Electrodes; Interruption; Knowledge; Lead; Learning; Maps; Mentors; Modeling; Monkeys; Nervous System Physiology; Neurons; Parietal Lobe; Periodicity; Prefrontal Cortex; Probability; Role; Sampling; Sensory; Signal Transduction; Social Interaction; Stimulus; Testing; Theoretical model; Training; Update; Visual Cortex; area V4; autism spectrum disorder; base; cell assembly; cognitive function; expectation; experience; experimental study; flexibility; insight; model building; network models; neural circuit; neural network; neuromechanism; neurophysiology; novel therapeutics; optogenetics; receptive field; relating to nervous system; social; theories

Project Summary A key cognitive function is expectation. Expectation is thought to be generated through an agent’s experiences and learning. An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing expectations) of the environment. The brain does this by forming predictions (PD). These predictions interact with incoming sensory data. When the PD matches the sensed data, the expectation is correct. When they do not match, a prediction error (PE) signal is generated. This PE signal is then used to update the prediction, so that the brain’s internal model can more optimally predict future sensory data. The implications for the predictive coding model are far-reaching. If the model is correct, it would fundamentally shift our understanding of the neural code from one that represents the “state of the environment” (e.g., the classic Hubel and Wiesel receptive field model) to one in which the brain performs “active sensing” and builds internal models of the world, testing them against incoming sensory data. In addition, the predictive coding model has many implications for our understanding of disease states. For example, autism can be understood as a failure in correctly predicting social actions, and as a result, every social interaction is “surprising”. Various theories exist about how a predictive code could be implemented in the brain. They propose that distinct cortical layers, flow of communication (feedforward/feedback), and oscillatory dynamics are involved in signaling PEs and PDs. However, little neurophysiological data exist to support these models. In the K99 portion of this grant, I manipulated predictions by changing the probabilities associated with objects in a delayed-match-to-sample task (Aim 1). This allowed me to induce expectations of varying strengths. With my primary mentor, Earl Miller, I was trained to perform make multi-area, multi-laminar recordings in monkeys. I then used these data to study how expectations are built and what happens when they are violated. In Aim 2, with my secondary mentor, Nancy Kopell, I used computational modeling to understand how the changing probability of inputs map on to a synchronously firing co-active group of cells (an assembly). We hypothesized that different assemblies represent different predictions. We also hypothesized that the strength of each assembly will represent the probability of a particular stimulus (thereby forming the neural basis of PD). Finally, due to the excitatory-inhibitory loops between cells in an assembly, we investigated whether re-activations of the assembly occur rhythmically, paced by a beta (15-30 Hz) oscillation in deep cortical layers. Gamma oscillations (40-90 Hz) in superficial cortical layers could help switch off the current prediction (PD) by signaling prediction error (PE). In Aim 3, an independent aim that will be my focus during the R00 portion of the grant, I will test whether interrupting beta oscillations (thought to signal PD) with closed-loop optogenetic inhibition is sufficient to disrupt the behavioral and neuronal signatures of prediction. These experiments are poised to significantly contribute to our understanding of predictive coding.

项目摘要 一个关键的认知功能是期望。期望被认为是通过代理人的经验和学习产生的。一个已建立的理论模型，预测编码，指出大脑不断建立环境模型（表示不断变化的期望）。大脑通过形成预测（PD）来做到这一点。这些预测与传入的感官数据相互作用。当PD与感测数据匹配时，预期是正确的。当它们不匹配时，生成预测误差（PE）信号。然后，这个PE信号被用来更新预测，以便大脑的内部模型可以更好地预测未来的感觉数据。 预测编码模型的影响是深远的。如果模型是正确的，它将从根本上改变我们对神经代码的理解，从一个代表“环境状态”（例如，从经典的Hubel和Wibel感受野模型）到大脑执行“主动感知”并建立世界的内部模型，并根据传入的感官数据对其进行测试的模型。此外，预测编码模型对我们理解疾病状态有许多意义。例如，自闭症可以被理解为无法正确预测社交行为，因此，每一次社交互动都是“令人惊讶的”。 关于如何在大脑中实现预测代码，存在各种理论。他们提出，不同的皮质层，通信流（前馈/反馈）和振荡动力学参与信号PE和PD。然而，几乎没有神经生理学数据支持这些模型。在K99部分，我通过改变与延迟匹配到样本任务（目标1）中的对象相关的概率来操纵预测。这使我能够诱导出不同力量的期望。在我的主要导师厄尔米勒的指导下，我接受了在猴子身上进行多区域、多层次录音的训练。然后，我用这些数据来研究期望是如何建立的，以及当它们被违反时会发生什么。在目标2中，我和我的第二位导师南希·科佩尔（Nancy Kopell）一起使用计算建模来理解输入的变化概率如何映射到一组同步激活的共同活性细胞（组装）上。我们假设不同的组合代表不同的预测。我们还假设，每个组件的强度将代表特定刺激的概率（从而形成PD的神经基础）。最后，由于组件中细胞之间的兴奋-抑制回路，我们研究了组件的再激活是否有节奏地发生，由深层皮质层中的β（15-30 Hz）振荡起搏。表层皮层中的伽马振荡（40-90 Hz）可以通过发出预测误差（PE）信号来帮助关闭当前预测（PD）。在目标3中，一个独立的目标将是我在R 00部分的重点，我将测试是否用闭环光遗传学抑制中断β振荡（被认为是PD信号）足以破坏预测的行为和神经元特征。这些实验将大大有助于我们对预测编码的理解。